BrainEffex: an interactive web application for exploring typical fMRI effect size estimates
Presented During:
Friday, June 27, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
M3 (Mezzanine Level)
Poster No:
1822
Submission Type:
Abstract Submission
Authors:
Hallee Shearer1, Matt Rosenblatt2, Jean Ye3, Rongtao Jiang4, Link Tejavibulya3, Maya Foster3, Qinghao Liang3, Javid Dadashkarimi5, Margaret Westwater3, Iris Cheng3, Alexandra Fischbach1, Ashley Humphries6, Aneesh Kumar1, Max Rolison3, Hannah Peterson2, Brendan Adkinson4, Saloni Mehta3, Chris Camp3, Thomas Nichols7, Joshua Curtiss1, Dustin Scheinost4, Stephanie Noble1
Institutions:
1Northeastern University, Boston, MA, 2Yale University, New Havent, CT, 3Yale University, New Haven, CT, 4Yale School of Medicine, New Haven, CT, 5Massachusetts General Hospital, Boston, MA, 6University of Nebraska-Lincoln, Lincoln, NE, 7University of Oxford, Oxford, Oxfordshire
First Author:
Co-Author(s):
Introduction:
Estimating effect size is a critical step in power analyses, and can help inform experimental design. However, effect size estimation is particularly difficult for fMRI data due to the complexity of the data and the analysis techniques. Further, it is difficult to obtain estimates from the literature, and small sample sizes of pilot studies may not provide precise enough estimates. When similar studies can be found in the literature, effect sizes are often not reported across the whole brain, limiting utility for study design. To facilitate the estimation and exploration of effect sizes for fMRI, we estimated effects for "typical" study designs with large datasets.
Methods:
Using large (n>500) subject-level datasets provided by contributors, we conducted brain-behavior correlations, task vs. rest contrasts, and between-group analyses with both functional connectivity and task-based activation maps. As of December 2024, the following datasets are included: the Adolescent Brain Cognitive Development study (ABCD), the Human Connectome Project (HCP), the Philadelphia Neurodevelopmental Cohort (PNC), the Healthy Brain Network (HBN), and the UK Biobank (UKB) (Alexander et al., 2017; Casey et al., 2018; Miller et al., 2016; Satterthwaite et al., 2014; Van Essen et al., 2013). The analyses leverage fMRI data from rest and commonly used tasks, and behavioral data reflecting various phenotypes. In light of recent research supporting the promise of broader-level methods (Marek et al., 2022; Noble et al., 2022), we included network-level and multivariate versions of all analyses. We repeated analyses with four motion deconfounding strategies: statistical control, full residualization, thresholding, and no correction. Results were transformed to Cohen's d and R2 estimates of effect size and simultaneous confidence intervals were calculated for each estimate.
Results:
The resulting effect maps from 78 studies were transformed into an interactive web application (BrainEffeX; Figure 1) for exploring, summarizing, and downloading these results (neuroprismlab.shinyapps.io/effect_size_shiny). The analysis pipeline and app were both intentionally designed to support the growth of this resource as more data become available. We welcome contributions of large (n > 500) subject-level fMRI datasets that can be incorporated into BrainEffeX. The structure of data contributions is provided in Figure 2.
Conclusions:
In summary, BrainEffeX is an interactive Shiny app designed to enable researchers to estimate typical effect sizes for fMRI studies and produce user-relevant summaries of effect size data. This tool is the first to our knowledge to enable users to interactively explore a wide range of "standard" effect sizes in functional neuroimaging. Recognizing that the currently available studies may not address the needs of all users, we have intentionally designed BrainEffeX as a growing resource with plans to incorporate new contributed data as they become available. By providing a reference for expected effect sizes in the field and the development of related tools, BrainEffeX will serve as a springboard for improved study planning and reproducible research in the field.
Modeling and Analysis Methods:
Methods Development 2
Other Methods
Neuroinformatics and Data Sharing:
Databasing and Data Sharing 1
Informatics Other
Novel Imaging Acquisition Methods:
Imaging Methods Other
Keywords:
Computational Neuroscience
Data analysis
Design and Analysis
FUNCTIONAL MRI
Informatics
Multivariate
Open Data
Open-Source Code
Statistical Methods
Other - Effect size
1|2Indicates the priority used for review
Abstract Information
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Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Are you Internal Review Board (IRB) certified? Please note: Failure to have IRB, if applicable will lead to automatic rejection of abstract.
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Please indicate which methods were used in your research:
Provide references using APA citation style.
Casey, B. (2018). The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites. Developmental Cognitive Neuroscience, 32, 43–54. https://doi.org/10.1016/j.dcn.2018.03.001
Marek, S. (2022). Reproducible brain-wide association studies require thousands of individuals. Nature, 603(7902), 654–660. https://doi.org/10.1038/s41586-022-04492-9
Miller, K. L. (2016). Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nature Neuroscience, 19(11), 1523–1536. https://doi.org/10.1038/nn.4393
Noble, S. (2022). Improving power in functional magnetic resonance imaging by moving beyond cluster-level inference. Proceedings of the National Academy of Sciences, 119(32), e2203020119. https://doi.org/10.1073/pnas.2203020119
Satterthwaite, T. D. (2014). Neuroimaging of the Philadelphia Neurodevelopmental Cohort. NeuroImage, 86, 544–553. https://doi.org/10.1016/j.neuroimage.2013.0